SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 121–130 of 903 papers

TitleStatusHype
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
A data-centric approach for assessing progress of Graph Neural NetworksCode1
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest—0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification—0
Sequential Binary Classification for Intrusion Detection—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified